A comprehensive investigation of LSTM-CNN deep learning model for fast detection of combustion instability

In this paper, we propose a deep learning model to detect combustion instability using high-speed flame image sequences. The detection model combines Convolutional Neural Network (CNN) and Long Short-Term Memory network (LSTM) to learn both spatial features and temporal correlations from high-speed images, and then outputs combustion instability detection results. We also visualize the extracted spatial features and their temporal evolution to interpret the detection process of model. In addition, we discuss the effect of different complexity of CNN layers and different amounts of training data on model performance. The proposed method achieves superior performance under various combustion conditions in swirl chamber with high accuracy and a short processing time about 1.23 ms per frame. Hence, we show that the proposed deep learning model is a promising detection tool for combustion instability under various combustion conditions.

Automated summary

The paper proposes a deep learning model combining CNN and LSTM to detect combustion instability using high-speed flame image sequences. The model achieves superior performance under various combustion conditions in swirl chamber, with high accuracy and short processing time per frame.